Wheat yield prediction using machine learning and advanced sensing techniques

Understanding yield limiting factors requires high resolution multi-layer information about factors affecting crop growth and yield. Therefore, on-line proximal soil sensing for estimation of soil properties is required, due to the ability of these sensors to collect high resolution data (>1500 sample per ha), and subsequently reducing labor and time cost of soil sampling and analysis. The aim of this paper is to predict within field variation in wheat yield, based on on-line multi-layer soil data, and satellite imagery crop growth characteristics. Supervised self-organizing maps capable of handling existent information from different soil and crop sensors by utilizing an unsupervised learning algorithm were used. The performance of counter-propagation artificial neural networks (CP-ANNs), XY-fused Networks (XY-Fs) and Supervised Kohonen Networks (SKNs) for predicting wheat yield in a 22 ha field in Bedfordshire, UK were compared for a single cropping season. The self organizing models consisted of input nodes corresponded to feature vectors formed from normalized values of on-line predicted soil parameters and the satellite normalized difference vegetation index (NDVI). The output nodes consisted of yield isofrequency classes, which were predicted from the three trained networks. Results showed that cross validation based yield prediction of the SKN model for the low yield class exceeded 91% which can be considered as highly accurate given the complex relationship between limiting factors and the yield. The medium and high yield class reached 70% and 83% respectively. The average overall accuracy for SKN was 81.65%, for CP-ANN 78.3% and for XY-F 80.92%, showing that the SKN model had the best overall performance. (C) 2015 Elsevier B.V. All rights reserved.